Statistics How To

Statistical Stability: Definition, Examples

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Statistical stability is how well the results of your study or experiment hold up.


More specifically, it’s a measure of how well you control for random errors in your study.

Statistical stability can be defined more precisely for specific fields. For example, let’s say you’re working with signal-to-noise ratio. Josselin Garnier and George Papanicolaou, in the book Passive Imaging with Ambient Noise, describe it as meaning
“…high signal-to-noise ratio of the quantity considered, including the image itself.”

How Do I Make Sure My Results Have Statistical Stability?

statistical stability

A p-value is an area in the tail of a distribution that tells you the odds of a result happening by chance.

Ways to ensure statistical stability to test your null hypothesis include:

  • p-values. A p value is used in hypothesis testing to support or reject the null hypothesis. It is the evidence against a null hypothesis. In general, the smaller the p-value the better.
  • confidence intervals. For example, you might report a 95% confidence interval with your results.

References

Aschengrau, A. & Seage, G. Essentials of Epidemiology in Public Health. Retrieved September 18, 2019 from: https://books.google.com/books?id=QelGjoKOWTAC
Josselin Garnier and George Papanicolaou. Passive Imaging with Ambient Noise,Retrieved September 18, 2019 from: https://books.google.com/books?id=9jrzCwAAQBAJ

CITE THIS AS:
Stephanie Glen. "Statistical Stability: Definition, Examples" From StatisticsHowTo.com: Elementary Statistics for the rest of us! https://www.statisticshowto.com/statistical-stability/
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